# person-vehicle-bike-detection-2004¶

## Use Case and High-Level Description¶

This is a person, vehicle, bike detector that is based on MobileNetV2 backbone with ATSS head for 448x256 resolution.

## Specification¶

Metric

Value

AP @ [ IoU=0.50:0.95 ]

0.274 (internal test set)

GFlops

1.811

MParams

2.327

Source framework

PyTorch*

Average Precision (AP) is defined as an area under the precision/recall curve.

## Inputs¶

Image, name: input, shape: 1, 3, 256, 448 in the format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Outputs¶

1. The boxes is a blob with the shape 100, 5 in the format N, 5, where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf], where:

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner

• conf - confidence for the predicted class

2. The labels is a blob with the shape 100 in the format N, where N is the number of detected bounding boxes. The value of each label is equal to predicted class ID (0 - vehicle, 1 - person, 2 - non-vehicle).

## Training Pipeline¶

The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.

## Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: